CFW: A Collaborative Filtering System Using Posteriors Over Weights of
Evidence

Carl M. Kadie, Christopher Meek, & David Heckerman

Microsoft Research

Abstract

We describe CFW, a computationally efficient algorithm for collaborative
filtering that uses posteriors over weights of evidence. In experiments on real
data, we show that this method predicts as well or better than other methods in
situations where the size of the user query is small. The new approach works
particularly well when the user's query contains low frequency (unpopular)
items. The approach complements that of dependency networks which perform well
when the size of the query is large. Also in this paper, we argue that the use
of posteriors over
weights of evidence is a natural way to recommend similar items---a task that is
somewhat different from the usual collaborative-filtering task.